Functional genomics is an emerging field in biotechnology that focuses on the characterization of gene function. All organisms contain only one genotype. However, the expression of this genotype under varying developmental and environmental conditions results in an almost infinite number of possible phenotypes. It is the correlation of gene expression to phenotype that defines functional genomics. To properly study a gene we need to not only know its identity (i.e. sequence) but to be able to observe and characterize its expression patterns in response to developmental and environmental changes, in isolation as well as in relation to the other genes in the genome. To properly study the effects resulting from the expression of a gene we need to be able to characterize the phenotype resulting from this activity in an objective and quantifiable manner. This is what the non-targeted metabolic profiling technology invention described herein enables the functional genomics community to do.
The gene sequences of entire species are now known. Gene-chip technology has made it possible to monitor and quantify the changes in expression of each and every gene within the genome to developmental and environmental changes, simultaneously. Gene-chip technology is, in essence, non-targeted gene expression analysis even though it is, in actuality, a targeted analysis that just so happens to contain all of the possible targets. This is a powerful comprehensive capability, but it was made possible by the fact that the genome is a finite and unitary entity. The analogous phenotypic capability would be to have every metabolite and protein of an organism known and on a chip. This is not possible due to the fact that not only are there multiple phenotypes, but a virtually infinite number of metabolites and proteins are possible. To be complementary to the current state of genomic analysis, phenotypic analysis must be non-targeted in “actuality”. The non-targeted metabolic profiling technology described herein is the only platform that satisfies the requirements of non-targeted phenotypic analysis. Furthermore, this technology is not restricted to any one species, but is equally effective in all plant and animal species.
Deciphering the complex molecular makeup of an individual phenotype is a formidable task. To be able to accurately and reproducibly generate this phenotypic information in such a way that the virtually infinite number of possible phenotypes can be compared to one another and correlated to gene expression is the crux of the dilemma that faces functional genomics. On the molecular level, the phenotype of a given biological system can be divided into the proteome and the metabolome. Since gene expression results in protein synthesis, the proteome is the first and most direct link to gene expression. However, due to the complex interactions of metabolic pathways, it is difficult to predict the effects that changes in the expression levels of a given protein will have on the overall cellular processes that it may be involved in. The metabolome, on the other hand, is the summation of all metabolic (proteomic) activities occurring in an organism at any given point in time. The metabolome is therefore a direct measure of the overall or end effect of gene expression on the cellular processes of any given biological system at any given time. For this reason, the metabolome should prove to be the more powerful of the two phenotypes in actually understanding the effects of gene function and manipulation. The non-targeted metabolic profiling technology described herein is the only comprehensive metabolic profiling technology available.
Isolation, identification, and quantitation are the three fundamental requirements of all analytical methods. The primary challenge for a non-targeted metabolome analysis is to meet these requirements for all of the metabolites in the metabolome, simultaneously. The second and perhaps more difficult challenge is to be able to meet these requirements with sufficient throughput and long-term stability such that it can be used side by side with gene-chip technology. Such technology will drastically reduce the time that is required for the function of a particular gene to be elucidated. In addition, databases of such analyses enable very large numbers of phenotypes and genotypes to be objectively and quantitatively compared. There is no such product or technology available to functional genomics scientists at this time. The non-targeted metabolic profiling technology described herein has been extensively tested in multiple species. In all cases, the technology has verified the metabolic variations known to exist between various genotypes and developmental stages of different species.
Key Technology Concept. The non-targeted metabolic profiling technology described herein can separate, quantify and identify all of the components in a complex biological sample quickly and simultaneously. This is achieved without any a priori selection of the metabolites of interest and is therefore unbiased. These data are exported to a database that allows the researcher to directly compare one sample to another (i.e. mutant vs. wild-type, flowering vs. stem elongation, drought stress vs. normal growing conditions, etc.) or to organize the entire database by metabolite concentration (i.e. which genotype has the greatest or least expression of a given metabolite). This technology is equally applicable to the study of human disease. To make use of this information, the researcher just types in the empirical formula (s) or the accurate mass(es) of the metabolite(s) he or she is interested in and the software will organize the data accordingly.
The ability to conduct an analysis of the composition of substances in biological samples is critical to many aspects of health care, environmental monitoring as well as the product development process. Typically the amount of a specific substance in a complex mixture is determined by various means. For example, in order to measure analytes in a complex mixture, the analyte(s) of interest must be separated from all of the other molecules in the mixture and then independently measured and identified.
In order to separate the analytes in a complex mixture from one another, unique chemical and/or physical characteristics of each analyte are used by the researcher to resolve the analytes from one another. These unique characteristics are also used to identify the analytes. In all previously published reports of complex mixture analysis, the methodologies require known analytical standards of each potential analyte before the presence and/or identity of a component in the unknown sample can be determined. The analytical standard(s) and the unknown sample(s) are processed in an identical manner through the method and the resulting characteristics of these standards recorded (for example: chromatographic retention time). Using this information, a sample containing unknown components can be analyzed and if a component in the unknown sample displays the same characteristic as one of the known analytical standard (s), the component is postulated to be the same entity as the analytical standard. This is targeted analysis technology. Targeted analysis technology is one-way. The researcher can go from known standard to methodology characteristics but not from methodology characteristics to known standard. The researcher can only confirm or refute the presence and/or amount of one of the previously analyzed standards. The researcher cannot go from the method characteristics of an unknown analyte to its chemical identity. The major drawback of this type of analysis is that any molecule that was not identified prior to analysis is not measured. As a result, much potentially useful information is lost to the researcher. To be truly non-targeted, the method must allow the researcher to equally evaluate all of the components of the mixture, whether they are known or unknown. This is only possible if the defining physical and/or chemical characteristics of the analyte are not related to the method of analysis but are inherent in the composition of the analyte itself (i.e. its atomic composition and therefore its accurate mass).
Key Benefits of Non-Targeted Metabolic Profiling Technology
1. Multidisciplinary. Virtually only one set of analyses would need to be performed on a given sample and the data resulting from this analysis would be available to all scientists regardless of the area of research they are focusing on.
2. Comprehensive. The non-targeted approach assesses ALL metabolite changes and will thus lead to a faster and more accurate determination of gene function/disfunction.
3. Unknown Metabolite Discovery. The non-targeted approach has the potential of identifying key metabolic regulators that are currently unknown, and which would not be monitored in a targeted analysis scenario.
4. High Throughput. The system is can be fully automated and analysis time is short allowing 100's of samples to be analyzed per instrument per day.
5. Quantitative. The system is reproducible and has an effective dynamic range >104. Relative changes in metabolite expression over entire populations can be studied.
Business Impact of Technology. The ability to generate searchable databases of the metabolic profiles of a given organism will represent a revolution in how the effects of genetic manipulation on a species can be studied. Currently our knowledge of the actual genetic code is much greater that our knowledge of the functions of the genes making up this code. After the mapping of the genome, the next greatest challenge will be determining the function and purpose of these gene products and how manipulation of these genes and their expression can be achieved to serve any number of purposes. The time, energy, and cost of investigating the effects of genetic manipulation are great. A database that can be searched for multiple purposes and which contains direct measures of the metabolic profiles of specific genotypes has the potential to dramatically decrease the amount of time required to determine the function of particular gene products. Such a database will reduce the risk of investing a large amount of time and resources researching genes which may have effects on protein expression, but due to down-stream feedback mechanisms, no net effect on metabolism at the whole cell or organism level.
In an article published in CURRENT OPINION IN PLANT BIOLOGY in 1999 entitled “Metabolic Profiling: a Rosetta Stone for genomics?”, Trethewey, Krotzky and Willmitzer indicated that exponential developments in computing have opened up the “possibility” of conducting non-targeted experimental science. While recognizing that it would not be possible to work with infinite degrees of freedom, the opinion was advanced that the power of post-experimental data processing would make possible this non-targeted approach. The non-targeted approach described in that article dealt only with the post acquisition analysis of metabolite data; not the non-targeted collection of metabolite data.
Thus the feasibility of non-targeted analysis of complex mixtures is neither obvious nor simple. The three major problems surrounding the non-targeted analysis of complex mixtures are: the ability to separate and identify all of the components in the mixture; the ability to organize the large amounts of data generated from the analysis into a format that can be used for research; and the ability to acquire this data in an automated fashion and in a reasonable amount of time.